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2020-07-06
Chegenizadeh, Mostafa, Ali, Mohammad, Mohajeri, Javad, Aref, Mohammad Reza.  2019.  An Anonymous Attribute-based Access Control System Supporting Access Structure Update. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :85–91.
It is quite common nowadays for clients to outsource their personal data to a cloud service provider. However, it causes some new challenges in the area of data confidentiality and access control. Attribute-based encryption is a promising solution for providing confidentiality and fine-grained access control in a cloud-based cryptographic system. Moreover, in some cases, to preserve the privacy of clients and data, applying hidden access structures is required. Also, a data owner should be able to update his defined access structure at any time when he is online or not. As in several real-world application scenarios like e-health systems, the anonymity of recipients, and the possibility of updating access structures are two necessary requirements. In this paper, for the first time, we propose an attribute-based access control scheme with hidden access structures enabling the cloud to update access structures on expiry dates defined by a data owner.
Attarian, Reyhane, Hashemi, Sattar.  2019.  Investigating the Streaming Algorithms Usage in Website Fingerprinting Attack Against Tor Privacy Enhancing Technology. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :33–38.
Website fingerprinting attack is a kind of traffic analysis attack that aims to identify the URL of visited websites using the Tor browser. Previous website fingerprinting attacks were based on batch learning methods which assumed that the traffic traces of each website are independent and generated from the stationary probability distribution. But, in realistic scenarios, the websites' concepts can change over time (dynamic websites) that is known as concept drift. To deal with data whose distribution change over time, the classifier model must update its model permanently and be adaptive to concept drift. Streaming algorithms are dynamic models that have these features and lead us to make a comparison of various representative data stream classification algorithms for website fingerprinting. Given to our experiments and results, by considering streaming algorithms along with statistical flow-based network traffic features, the accuracy grows significantly.
2020-07-03
Libicki, Martin.  2019.  For a Baltic Cyberspace Alliance? 2019 11th International Conference on Cyber Conflict (CyCon). 900:1—14.

In NATO, an attack on one is an attack on all. In recent years, this tenet has been extended to mean that a cyberattack on one is a cyberattack on all. But does what makes sense in the physical world also make sense if extended into cyberspace? And if there is virtue in collective cyberspace defense, is NATO necessarily the right grouping - in a world where, as far as the United States and the United Kingdom are concerned, more of what constitutes cyber defense circulates within the Five Eyes coalition rather than within NATO? To explore these issues, this essay moots the creation of a Baltic-area cyberspace alliance, considers what it would do, assesses its costs and benefits for its members, and concludes by considering whether such an alliance would be also be in the interest of the U.S. Keys to this discussion are (1) the distinction between what constitutes an “attack” in a medium where occupation may result and actions in media where occupation is (currently) meaningless and effects almost always reversible, (2) what collective defense should mean in cyberspace - and where responsibilities may be best discharged within the mix of hardness, pre-emption, and deterrence that constitute defense, (3) the relationship between cyberspace defense and information warfare defense, and (4) the relevance to alliance formation of the fact that while war is dull, dirty, and dangerous, cyber war is none of these three.

Fitwi, Alem, Chen, Yu, Zhu, Sencun.  2019.  A Lightweight Blockchain-Based Privacy Protection for Smart Surveillance at the Edge. 2019 IEEE International Conference on Blockchain (Blockchain). :552—555.

Witnessing the increasingly pervasive deployment of security video surveillance systems(VSS), more and more individuals have become concerned with the issues of privacy violations. While the majority of the public have a favorable view of surveillance in terms of crime deterrence, individuals do not accept the invasive monitoring of their private life. To date, however, there is not a lightweight and secure privacy-preserving solution for video surveillance systems. The recent success of blockchain (BC) technologies and their applications in the Internet of Things (IoT) shed a light on this challenging issue. In this paper, we propose a Lightweight, Blockchain-based Privacy protection (Lib-Pri) scheme for surveillance cameras at the edge. It enables the VSS to perform surveillance without compromising the privacy of people captured in the videos. The Lib-Pri system transforms the deployed VSS into a system that functions as a federated blockchain network capable of carrying out integrity checking, blurring keys management, feature sharing, and video access sanctioning. The policy-based enforcement of privacy measures is carried out at the edge devices for real-time video analytics without cluttering the network.

Zhang, Yonghong, Zheng, Peijia, Luo, Weiqi.  2019.  Privacy-Preserving Outsourcing Computation of QR Decomposition in the Encrypted Domain. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :389—396.
Signal processing in encrypted domain has become an important mean to protect privacy in an untrusted network environment. Due to the limitations of the underlying encryption methods, many useful algorithms that are sophisticated are not well implemented. Considering that QR decomposition is widely used in many fields, in this paper, we propose to implement QR decomposition in homomorphic encrypted domain. We firstly realize some necessary primitive operations in homomorphic encrypted domain, including division and open square operation. Gram-Schmidt process is then studied in the encrypted domain. We propose the implementation of QR decomposition in the encrypted domain by using the secure implementation of Gram-Schmidt process. We conduct experiments to demonstrate the effectiveness and analyze the performance of the proposed outsourced QR decomposition.
Abbasi, Milad Haji, Majidi, Babak, Eshghi, Moahmmad, Abbasi, Ebrahim Haji.  2019.  Deep Visual Privacy Preserving for Internet of Robotic Things. 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI). :292—296.
In the past few years, visual information collection and transmission is increased significantly for various applications. Smart vehicles, service robotic platforms and surveillance cameras for the smart city applications are collecting a large amount of visual data. The preservation of the privacy of people presented in this data is an important factor in storage, processing, sharing and transmission of visual data across the Internet of Robotic Things (IoRT). In this paper, a novel anonymisation method for information security and privacy preservation in visual data in sharing layer of the Web of Robotic Things (WoRT) is proposed. The proposed framework uses deep neural network based semantic segmentation to preserve the privacy in video data base of the access level of the applications and users. The data is anonymised to the applications with lower level access but the applications with higher legal access level can analyze and annotated the complete data. The experimental results show that the proposed method while giving the required access to the authorities for legal applications of smart city surveillance, is capable of preserving the privacy of the people presented in the data.
2020-06-29
Jamader, Asik Rahaman, Das, Puja, Acharya, Biswa Ranjan.  2019.  BcIoT: Blockchain based DDos Prevention Architecture for IoT. 2019 International Conference on Intelligent Computing and Control Systems (ICCS). :377–382.
The Internet of Things (IoT) visualizes a massive network with billions of interaction among smart things which are capable of contributing all sorts of services. Self-configuring things (nodes) are connected dynamically with a global network in IoT scenario. The small things are widely spread in a real world paradigm with minimal processing capacity and limited storage. The recent IoT technologies have more concerns about the security, privacy and reliability. Sharing personal data over the centralized system still remains as a challenging task. If the infrastructure is able to provide the assurance for transferring the data but for now it requires special attention on security and data consistency. Because, centralized system and infrastructure is viewed as a more attractive point for hacker or cyber-attacker. To solve this we present a secured smart contract based on Blockchain to develop a secured communicative network. A Hash based secret key is used for encryption and decryption purposes. A demo attack is done for developing a better understanding on blockchain technology in terms of their comparison and calculation.
2020-06-26
Shengquan, Wang, Xianglong, Li, Ang, Li, Shenlong, Jiang.  2019.  Research on Iris Edge Detection Technology based on Daugman Algorithm. 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :308—311.

In the current society, people pay more and more attention to identity security, especially in the case of some highly confidential or personal privacy, one-to-one identification is particularly important. The iris recognition just has the characteristics of high efficiency, not easy to be counterfeited, etc., which has been promoted as an identity technology. This paper has carried out research on daugman algorithm and iris edge detection.

Bouchaala, Mariem, Ghazel, Cherif, Saidane, Leila Azouz.  2019.  Revocable Sliced CipherText Policy Attribute Based Encryption Scheme in Cloud Computing. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :1860—1865.

Cloud Computing is the most promising paradigm in recent times. It offers a cost-efficient service to individual and industries. However, outsourcing sensitive data to entrusted Cloud servers presents a brake to Cloud migration. Consequently, improving the security of data access is the most critical task. As an efficient cryptographic technique, Ciphertext Policy Attribute Based Encryption(CP-ABE) develops and implements fine-grained, flexible and scalable access control model. However, existing CP-ABE based approaches suffer from some limitations namely revocation, data owner overhead and computational cost. In this paper, we propose a sliced revocable solution resolving the aforementioned issues abbreviated RS-CPABE. We applied splitting algorithm. We execute symmetric encryption with Advanced Encryption Standard (AES)in large data size and asymmetric encryption with CP-ABE in constant key length. We re-encrypt in case of revocation one single slice. To prove the proposed model, we expose security and performance evaluation.

B M, Chandrakala, Linga Reddy, S C.  2019.  Proxy Re-Encryption using MLBC (Modified Lattice Based Cryptography). 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC). :1—5.
In last few years, Proxy Re-Encryption has been used for forwarding the encrypted message to the user, these users are the one who has not been a part of encryption. In the past several scheme were developed in order to provide the efficient and secure proxy re-encryption. However, these methodology mainly focused on features like maximum key privacy, minimal trust proxy and others. In such cases the efficiency and security was mainly ignored. Hence, in order to provide the efficient and secure proxy re-encryption, we proposed an algorithm named as MLBC (Modified Lattice Based Cryptography) is proposed. Our method is based on the PKE (Public Key Encryption) and it provides more efficiency when compared to the other cryptography technique. Later in order to evaluate the algorithm simulation is done based on several parameter such as encryption time, proxy key generation time, Re-encryption time and Total computation time. Later, it is compared with the existing algorithm and the plotted graph clearly shows that our algorithm outperforms the existing algorithm.
2020-06-22
Adesuyi, Tosin A., Kim, Byeong Man.  2019.  Preserving Privacy in Convolutional Neural Network: An ∊-tuple Differential Privacy Approach. 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII). :570–573.
Recent breakthrough in neural network has led to the birth of Convolutional neural network (CNN) which has been found to be very efficient especially in the areas of image recognition and classification. This success is traceable to the availability of large datasets and its capability to learn salient and complex data features which subsequently produce a reusable output model (Fθ). The Fθ are often made available (e.g. on cloud as-a-service) for others (client) to train their data or do transfer learning, however, an adversary can perpetrate a model inversion attack on the model Fθ to recover training data, hence compromising the sensitivity of the model buildup data. This is possible because CNN as a variant of deep neural network does memorize most of its training data during learning. Consequently, this has pose a privacy concern especially when a medical or financial data are used as model buildup data. Existing researches that proffers privacy preserving approach however suffer from significant accuracy degradation and this has left privacy preserving model on a theoretical desk. In this paper, we proposed an ϵ-tuple differential privacy approach that is based on neuron impact factor estimation to preserve privacy of CNN model without significant accuracy degradation. We experiment our approach on two large datasets and the result shows no significant accuracy degradation.
Feng, Tianyi, Wong, Wai-Choong, Sun, Sumei, Zhao, Yonghao, Zhang, Zhixiang.  2019.  Location Privacy Preservation and Location-based Service Quality Tradeoff Framework Based on Differential Privacy. 2019 16th Workshop on Positioning, Navigation and Communications (WPNC). :1–6.
With the widespread use of location-based services and the development of localization systems, user's locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users' location privacy and provide location-based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users' true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.
Triastcyn, Aleksei, Faltings, Boi.  2019.  Federated Learning with Bayesian Differential Privacy. 2019 IEEE International Conference on Big Data (Big Data). :2587–2596.
We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We adapt the Bayesian privacy accounting method to the federated setting and suggest multiple improvements for more efficient privacy budgeting at different levels. Our experiments show significant advantage over the state-of-the-art differential privacy bounds for federated learning on image classification tasks, including a medical application, bringing the privacy budget below ε = 1 at the client level, and below ε = 0.1 at the instance level. Lower amounts of noise also benefit the model accuracy and reduce the number of communication rounds.
Tong, Dong, Yong, Zeng, Mengli, Liu, Zhihong, Liu, Jianfeng, Ma, Xiaoyan, Zhu.  2019.  A Topology Based Differential Privacy Scheme for Average Path Length Query. 2019 International Conference on Networking and Network Applications (NaNA). :350–355.
Differential privacy is heavily used in privacy protection due to it provides strong protection against private data. The existing differential privacy scheme mainly implements the privacy protection of nodes or edges in the network by perturbing the data query results. Most of them cannot meet the privacy protection requirements of multiple types of information. In order to overcome these issues, a differential privacy security mechanism with average path length (APL) query is proposed in this paper, which realize the privacy protection of both network vertices and edge weights. Firstly, by describing APL, the reasons for choosing this attribute as the query function are analyzed. Secondly, global sensitivity of APL query under the need of node privacy protection and edge-weighted privacy protection is proved. Finally, the relationship between data availability and privacy control parameters in differential privacy is analyzed through experiments.
Lv, Chaoxian, Li, Qianmu, Long, Huaqiu, Ren, Yumei, Ling, Fei.  2019.  A Differential Privacy Random Forest Method of Privacy Protection in Cloud. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :470–475.
This paper proposes a new random forest classification algorithm based on differential privacy protection. In order to reduce the impact of differential privacy protection on the accuracy of random forest classification, a hybrid decision tree algorithm is proposed in this paper. The hybrid decision tree algorithm is applied to the construction of random forest, which balances the privacy and classification accuracy of the random forest algorithm based on differential privacy. Experiment results show that the random forest algorithm based on differential privacy can provide high privacy protection while ensuring high classification performance, achieving a balance between privacy and classification accuracy, and has practical application value.
Gao, Ruichao, Ma, Xuebin.  2019.  Dynamic Data Publishing with Differential Privacy via Reinforcement Learning. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:746–752.
Differential privacy, which is due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics with privacy protection. Recently, a lot of dynamic data publishing algorithms based on differential privacy have been proposed, but most of the algorithms use a native method to allocate the privacy budget. That is, the limited privacy budget is allocated to each time point uniformly, which may result in the privacy budget being unreasonably utilized and reducing the utility of data. In order to make full use of the limited privacy budget in the dynamic data publishing and improve the utility of data publishing, we propose a dynamic data publishing algorithm based on reinforcement learning in this paper. The algorithm consists of two parts: privacy budget allocation and data release. In the privacy budget allocation phase, we combine the idea of reinforcement learning and the changing characteristics of dynamic data, and establish a reinforcement learning model for the allocation of privacy budget. Finally, the algorithm finds a reasonable privacy budget allocation scheme to publish dynamic data. In the data release phase, we also propose a new dynamic data publishing strategy to publish data after the privacy budget is exhausted. Extensive experiments on real datasets demonstrate that our algorithm can allocate the privacy budget reasonably and improve the utility of dynamic data publishing.
2020-06-19
Lai, Chengzhe, Du, Yangyang, Men, Jiawei, Zheng, Dong.  2019.  A Trust-based Real-time Map Updating Scheme. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :334—339.

The real-time map updating enables vehicles to obtain accurate and timely traffic information. Especially for driverless cars, real-time map updating can provide high-precision map service to assist the navigation, which requires vehicles to actively upload the latest road conditions. However, due to the untrusted network environment, it is difficult for the real-time map updating server to evaluate the authenticity of the road information from the vehicles. In order to prevent malicious vehicles from deliberately spreading false information and protect the privacy of vehicles from tracking attacks, this paper proposes a trust-based real-time map updating scheme. In this scheme, the public key is used as the identifier of the vehicle for anonymous communication with conditional anonymity. In addition, the blockchain is applied to provide the existence proof for the public key certificate of the vehicle. At the same time, to avoid the spread of false messages, a trust evaluation algorithm is designed. The fog node can validate the received massages from vehicles using Bayesian Inference Model. Based on the verification results, the road condition information is sent to the real-time map updating server so that the server can update the map in time and prevent the secondary traffic accident. In order to calculate the trust value offset for the vehicle, the fog node generates a rating for each message source vehicle, and finally adds the relevant data to the blockchain. According to the result of security analysis, this scheme can guarantee the anonymity and prevent the Sybil attack. Simulation results show that the proposed scheme is effective and accurate in terms of real-time map updating and trust values calculating.

2020-06-08
Das, Bablu Kumar, Garg, Ritu.  2019.  Security of Cloud Storage based on Extended Hill Cipher and Homomorphic Encryption. 2019 International Conference on Communication and Electronics Systems (ICCES). :515–520.
Cloud computing is one of the emerging area in the business world that help to access resources at low expense with high privacy. Security is a standout amongst the most imperative difficulties in cloud network for cloud providers and their customers. In order to ensure security in cloud, we proposed a framework using different encryption algorithm namely Extended hill cipher and homomorphic encryption. Firstly user data/information is isolated into two parts which is static and dynamic data (critical data). Extended hill cipher encryption is applied over more important dynamic part where we are encrypting the string using matrix multiplication. While homomorphic encryption is applied over static data in which it accepts n number of strings as information, encode each string independently and lastly combine all the strings. The test results clearly manifests that the proposed model provides better information security.
2020-06-04
Gulhane, Aniket, Vyas, Akhil, Mitra, Reshmi, Oruche, Roland, Hoefer, Gabriela, Valluripally, Samaikya, Calyam, Prasad, Hoque, Khaza Anuarul.  2019.  Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications. 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC). :1—9.

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.

2020-06-02
Coiteux-Roy, Xavier, Wolf, Stefan.  2019.  Proving Erasure. 2019 IEEE International Symposium on Information Theory (ISIT). :832—836.

It seems impossible to certify that a remote hosting service does not leak its users' data - or does quantum mechanics make it possible? We investigate if a server hosting data can information-theoretically prove its definite deletion using a "BB84-like" protocol. To do so, we first rigorously introduce an alternative to privacy by encryption: privacy delegation. We then apply this novel concept to provable deletion and remote data storage. For both tasks, we present a protocol, sketch its partial security, and display its vulnerability to eavesdropping attacks targeting only a few bits.

Aliasgari, Malihe, Simeone, Osvaldo, Kliewer, Jörg.  2019.  Distributed and Private Coded Matrix Computation with Flexible Communication Load. 2019 IEEE International Symposium on Information Theory (ISIT). :1092—1096.

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. These operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a tradeoff between recovery threshold and communication load. In this work, we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.

2020-06-01
Mohd Ariffin, Noor Afiza, Mohd Sani, Noor Fazlida.  2018.  A Multi-factor Biometric Authentication Scheme Using Attack Recognition and Key Generator Technique for Security Vulnerabilities to Withstand Attacks. 2018 IEEE Conference on Application, Information and Network Security (AINS). :43–48.
Security plays an important role in many authentication applications. Modern era information sharing is boundless and becoming much easier to access with the introduction of the Internet and the World Wide Web. Although this can be considered as a good point, issues such as privacy and data integrity arise due to the lack of control and authority. For this reason, the concept of data security was introduced. Data security can be categorized into two which are secrecy and authentication. In particular, this research was focused on the authentication of data security. There have been substantial research which discusses on multi-factor authentication scheme but most of those research do not entirely protect data against all types of attacks. Most current research only focuses on improving the security part of authentication while neglecting other important parts such as the accuracy and efficiency of the system. Current multifactor authentication schemes were simply not designed to have security, accuracy, and efficiency as their main focus. To overcome the above issue, this research will propose a new multi-factor authentication scheme which is capable to withstand external attacks which are known security vulnerabilities and attacks which are based on user behavior. On the other hand, the proposed scheme still needs to maintain an optimum level of accuracy and efficiency. From the result of the experiments, the proposed scheme was proven to be able to withstand the attacks. This is due to the implementation of the attack recognition and key generator technique together with the use of multi-factor in the proposed scheme.
Jacomme, Charlie, Kremer, Steve.  2018.  An Extensive Formal Analysis of Multi-factor Authentication Protocols. 2018 IEEE 31st Computer Security Foundations Symposium (CSF). :1–15.
Passwords are still the most widespread means for authenticating users, even though they have been shown to create huge security problems. This motivated the use of additional authentication mechanisms used in so-called multi-factor authentication protocols. In this paper we define a detailed threat model for this kind of protocols: while in classical protocol analysis attackers control the communication network, we take into account that many communications are performed over TLS channels, that computers may be infected by different kinds of malwares, that attackers could perform phishing, and that humans may omit some actions. We formalize this model in the applied pi calculus and perform an extensive analysis and comparison of several widely used protocols - variants of Google 2-step and FIDO's U2F. The analysis is completely automated, generating systematically all combinations of threat scenarios for each of the protocols and using the P ROVERIF tool for automated protocol analysis. Our analysis highlights weaknesses and strengths of the different protocols, and allows us to suggest several small modifications of the existing protocols which are easy to implement, yet improve their security in several threat scenarios.
Alizai, Zahoor Ahmed, Tareen, Noquia Fatima, Jadoon, Iqra.  2018.  Improved IoT Device Authentication Scheme Using Device Capability and Digital Signatures. 2018 International Conference on Applied and Engineering Mathematics (ICAEM). :1–5.
Internet of Things (IoT) device authentication is weighed as a very important step from security perspective. Privacy and security of the IoT devices and applications is the major issue. From security perspective, important issue that needs to be addressed is the authentication mechanism, it has to be secure from different types of attacks and is easy to implement. The paper gives general idea about how different authentication mechanisms work, and then secure and efficient multi-factor device authentication scheme idea is proposed. The proposed scheme idea uses digital signatures and device capability to authenticate a device. In the proposed scheme device will only be allowed into the network if it is successfully authenticated through multi-factor authentication otherwise the authentication process fails and whole authentication process will restart. By analyzing the proposed scheme idea, it can be seen that the scheme is efficient and has less over head. The scheme not only authenticates the device very efficiently through multi-factor authentication but also authenticates the authentication server with the help of digital signatures. The proposed scheme also mitigates the common attacks like replay and man in the middle because of nonce and timestamp.
da Silva Andrade, Richardson B., Souto Rosa, Nelson.  2019.  MidSecThings: Assurance Solution for Security Smart Homes in IoT. 2019 IEEE 19th International Symposium on High Assurance Systems Engineering (HASE). :171–178.
The interest over building security-based solutions to reduce the vulnerability exploits and mitigate the risks associated with smart homes in IoT is growing. However, our investigation identified to architect and implement distributed security mechanisms is still a challenge because is necessary to handle security and privacy in IoT middleware with a strong focus. Our investigation, it was identified the significant proportion of the systems that did not address security and did not describe the security approach in any meaningful detail. The idea proposed in this work is to provide middleware aim to implement security mechanisms in smart home and contribute as how guide to beginner developers' IoT middleware. The advantages of using MidSecThings are to avoid leakage data, unavailable service, unidentification action and not authorized access over IoT devices in smart home.